Low-Power Sparse Hyperdimensional Encoder for Language Recognition

Online learning for data analysis, categorization and anomaly detection has become a key technique in a range of adaptive embedded applications. In this article the authors propose a very low power encoder design using a sparse, hyperdimensional representation of letters and words in natural language and they show that this representation and their design can be used with high energy efficiency for a language recognition task. — Axel Jantsch, Royal Institute of Technology Electronics, Computer and Software Systems